






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)



class     lowerCamelCase (unittest.TestCase     ):







		'''simple docstring'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase=1_3							,						_UpperCamelCase=7							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						_UpperCamelCase=9_9							,						_UpperCamelCase=3_2							,						_UpperCamelCase=5							,						_UpperCamelCase=4							,						_UpperCamelCase=3_7							,						_UpperCamelCase="gelu"							,						_UpperCamelCase=0.1							,						_UpperCamelCase=0.1							,						_UpperCamelCase=5_1_2							,						_UpperCamelCase=1_6							,						_UpperCamelCase=2							,						_UpperCamelCase=0.02							,						_UpperCamelCase=4							,						)      ->				str:
							UpperCAmelCase_       : Any				=		parent
							UpperCAmelCase_       : Any				=		batch_size
							UpperCAmelCase_       : Union[str, Any]				=		seq_length
							UpperCAmelCase_       : Dict				=		is_training
							UpperCAmelCase_       : Tuple				=		use_attention_mask
							UpperCAmelCase_       : Tuple				=		use_token_type_ids
							UpperCAmelCase_       : int				=		use_labels
							UpperCAmelCase_       : Optional[Any]				=		vocab_size
							UpperCAmelCase_       : Dict				=		hidden_size
							UpperCAmelCase_       : Optional[Any]				=		num_hidden_layers
							UpperCAmelCase_       : Dict				=		num_attention_heads
							UpperCAmelCase_       : List[Any]				=		intermediate_size
							UpperCAmelCase_       : Optional[int]				=		hidden_act
							UpperCAmelCase_       : int				=		hidden_dropout_prob
							UpperCAmelCase_       : List[Any]				=		attention_probs_dropout_prob
							UpperCAmelCase_       : Optional[int]				=		max_position_embeddings
							UpperCAmelCase_       : Any				=		type_vocab_size
							UpperCAmelCase_       : Optional[Any]				=		type_sequence_label_size
							UpperCAmelCase_       : List[str]				=		initializer_range
							UpperCAmelCase_       : Dict				=		num_choices



		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							UpperCAmelCase_       : Union[str, Any]				=		ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size  )

							UpperCAmelCase_       : str				=		None
							if self.use_attention_mask:
												UpperCAmelCase_       : List[str]				=		random_attention_mask([self.batch_size, self.seq_length]  )

							UpperCAmelCase_       : Dict				=		None
							if self.use_token_type_ids:
												UpperCAmelCase_       : str				=		ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size  )

							UpperCAmelCase_       : List[str]				=		RobertaPreLayerNormConfig(
							    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						is_decoder=_UpperCamelCase							,						initializer_range=self.initializer_range							,						)

							return config, input_ids, token_type_ids, attention_mask



		def    __UpperCAmelCase     (				self  )      ->				Union[str, Any]:
							UpperCAmelCase_       : Dict				=		self.prepare_config_and_inputs()
							UpperCAmelCase_       : str				=		config_and_inputs
							UpperCAmelCase_       : List[Any]				=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
							return config, inputs_dict



		def    __UpperCAmelCase     (				self  )      ->				int:
							UpperCAmelCase_       : List[str]				=		self.prepare_config_and_inputs()
							UpperCAmelCase_       : List[Any]				=		config_and_inputs

							UpperCAmelCase_       : Tuple				=		True
							UpperCAmelCase_       : Dict				=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
							UpperCAmelCase_       : Dict				=		ids_tensor([self.batch_size, self.seq_length]							,						vocab_size=2  )

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     lowerCamelCase (__lowercase					,    unittest.TestCase     ):







		'''simple docstring'''



		_snake_case       :       str					=    True

		_snake_case       :       Dict					=    (
		    (
		        FlaxRobertaPreLayerNormModel,
		        FlaxRobertaPreLayerNormForCausalLM,
		        FlaxRobertaPreLayerNormForMaskedLM,
		        FlaxRobertaPreLayerNormForSequenceClassification,
		        FlaxRobertaPreLayerNormForTokenClassification,
		        FlaxRobertaPreLayerNormForMultipleChoice,
		        FlaxRobertaPreLayerNormForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)



		def    __UpperCAmelCase     (				self  )      ->				List[str]:
							UpperCAmelCase_       : List[str]				=		FlaxRobertaPreLayerNormModelTester(self  )



		@slow
		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							for model_class_name in self.all_model_classes:
												UpperCAmelCase_       : Dict				=		model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'							,						from_pt=_UpperCamelCase  )
												UpperCAmelCase_       : str				=		model(np.ones((1, 1)  )  )
												self.assertIsNotNone(_UpperCamelCase  )





@require_flax
class     lowerCamelCase (unittest.TestCase     ):







		'''simple docstring'''



		@slow
		def    __UpperCAmelCase     (				self  )      ->				str:
							UpperCAmelCase_       : List[str]				=		FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'							,						from_pt=_UpperCamelCase  )

							UpperCAmelCase_       : List[Any]				=		np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							,						dtype=jnp.intaa  )
							UpperCAmelCase_       : Union[str, Any]				=		model(_UpperCamelCase  )[0]
							UpperCAmelCase_       : List[str]				=		[1, 1_1, 5_0_2_6_5]
							self.assertEqual(list(output.shape  )							,						_UpperCamelCase  )
							# compare the actual values for a slice.
							UpperCAmelCase_       : List[Any]				=		np.array(
							    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]							,						dtype=np.floataa  )
							self.assertTrue(np.allclose(output[:, :3, :3]							,						_UpperCamelCase							,						atol=1E-4  )  )



		@slow
		def    __UpperCAmelCase     (				self  )      ->				Optional[int]:
							UpperCAmelCase_       : Dict				=		FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'							,						from_pt=_UpperCamelCase  )

							UpperCAmelCase_       : List[str]				=		np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							,						dtype=jnp.intaa  )
							UpperCAmelCase_       : Any				=		model(_UpperCamelCase  )[0]
							# compare the actual values for a slice.
							UpperCAmelCase_       : Dict				=		np.array(
							    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]							,						dtype=np.floataa  )
							self.assertTrue(np.allclose(output[:, :3, :3]							,						_UpperCamelCase							,						atol=1E-4  )  )








import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)